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                           Call for Contributions
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                              AIPerf 2023
    1st Workshop on Artificial Intelligence for Performance
Modeling, Prediction, and Control co-located with ICPE 2023

                          April 15 or 16, 2023  
                         Coimbra, Portugal

               Web:  https://aiperf.github.io/aiperf2023/
    Submission: https://easychair.org/conferences/?conf=icpe2023

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IMPORTANT DATES
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Deadlines are at Midnight (AoE)

Paper Submission: January  23,  2023
Paper Notification of Acceptance: February  13,  2023
Camera-Ready Submission: February 20, 2023

SCOPE AND TOPICS
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AIPerf intends to foster the usage of AI (such as probabilistic methods, machine learning,
and deep learning) to control, model, and predict the performance of computer systems.
The timeliness and relevance of this topic reflect current and future trends toward exploiting
AI-based approaches to deal with complex, large, and interconnected systems. However,
their usage for performance modeling and evaluation is still limited, and their benefit to the
performance engineering field remains unclear.

AIPerf 2023 proposes a meeting venue to promote the dissemination of research works that use
or study AI techniques for quantitative analysis of modern ICT systems and to engage academics
and practitioners of this field. The workshop focuses on presenting experiences and results of
applying AI/ML-based techniques to performance-related problems, as well as sharing performance
datasets and benchmarks with the community to facilitate the development of new and more accurate
learning procedures.

Specifically, AIPerf 2023 welcomes submissions reporting methodological and practical research
that advances and promotes the usage of Artificial Intelligence for the performance evaluation
of ICT systems. The list of topics includes (but is not limited to):

    • AI-based performance evaluation and tuning for ICT systems
    • AI-based generation of performance models for ICT systems engineering
    • Performance modeling of AI systems and frameworks
    • Datasets and benchmarks for training and validating AI performance models
    • Explainable and White-box AI techniques for performance engineering
    • Heuristic and metaheuristic techniques for performance engineering
    • AI models for performance anomaly detection, classification, root cause analysis and remediation
    • AI models for performance tasks automation, including auto-scaling and self-optimization
    • Human computer interfaces for AI based performance tasks

AIPerf solicits two tracks of submission:

    • Regular research papers (max 8 pages). Novel contributions that are fully
      validated and well positioned in the state-of-the-art.
    • Empirical, experience, reproduction or case study papers (max 6 pages).
      Work-in-progress; Vision papers; New ideas that still need to be validated;
      Industrial case studies; Experiences (positive or negative) of using AI
      for performance engineering.

The workshop proceedings are published in ACM Digital Library as the ICPE2023 conference
companion post-proceedings. For more information visit: https://aiperf.github.io/aiperf2023/submission/

ORGANIZING COMMITTEE
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Workshop Chairs
    • Emilio Incerto, IMT School For Advanced Studies, Italy
    • Marin Litoiu, Lassonde School of Engineering, York University, Canada
    • Riccardo Pinciroli, Gran Sasso Science Institute, Italy
To contact the chairs, you can email: aiperf2023@easychair.org